The Radar Cross-Section (RCS) of an object of interest can be determined computationally, for example using an RCS prediction software application or empirically, for example in indoor radar ranges. Spurious data points in the computations or measurements often occur, and can be attributed to optimization of object model discretization for frequency sub-bands; discontinuities between RCS data from multiple sources, measurement error, and the presence of clutter in RCS measurements. Spurious data points adversely affect the time domain representation of the RCS data.
Spurious data points at boundaries between frequency sub-bands in calculated RCS data can be eliminated sometimes by recomputing the RCS using a model optimized for that frequency. Spurious data points due to measurement error can be eliminated sometimes by repeating measurements. However, recomputing the RCS using a differently optimized model or repeating RCS measurements is time-consuming. Depending on the model and the frequency range, RCS computations can take hundreds of hours to compute and are therefore costly. If eliminating the spurious data points is impractical or impossible, data points believed to be spurious can be ignored. However, ignoring data points can be risky, in that some true features of the object of interest may be masked.